docs: def load_images(image_files, resize=True):
    """Load images from files and optionally resize it."""
  
    images = []
    for image_file in image_files:
      with file_io.FileIO(image_file, 'r') as ff:
        images.append(ff.read())
    if resize is False:
      return images
  
    # To resize, run a tf session so we can reuse 'decode_and_resize()'
    # which is used in prediction graph. This makes sure we don't lose
    # any quality in prediction, while decreasing the size of the images
    # submitted to the model over network.
    image_str_tensor = tf.placeholder(tf.string, shape=[None])
    image = tf.map_fn(resize_image, image_str_tensor, back_prop=False)
    feed_dict = collections.defaultdict(list)
    feed_dict[image_str_tensor.name] = images
    with tf.Session() as sess:
      images_resized = sess.run(image, feed_dict=feed_dict)
    return images_resized